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Journal of Materials Engineering and Performance

Erschienen in:

08.03.2021

Parametric Analysis for Erosion Wear of Waste Marble Dust-Filled Polyester Using Response Surface Method and Neural Networks

verfasst von: Sandip Kumar Nayak, Alok Satapathy, Sisir Mantry

Erschienen in: Journal of Materials Engineering and Performance | Ausgabe 6/2021

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Abstract

In this research, the erosion wear characterization of waste marble dust (an industrial/construction waste)-filled polyester composites is evaluated. The relative effect of the control factors on the erosion rate of the composites is experimentally and statistically evaluated using a statistical model based on the response surface method, and the mechanisms of erosion loss are studied from the worn surface morphologies taken using a scanning electron microscope. The analysis reveals that striking velocity, filler concentration, and impingement angle in that sequence are the significant control factors affecting the erosion rate of the composites. The erosion efficiency of the composites is calculated to ascertain the erosion behavior of the composites. Further, an analytical as well as predictive model working on neural networks, is used to predict the erosion rate of the composites at different levels of the individual control factors. Such composites are expected to be advantageous in wear-related applications.

Vorheriger Artikel Structure, Morphology, Thermal Stability and Oxidation Resistance of Multilayered TiSiN/VN Films: Influence of TiSiN-Layer Thickness

Nächster Artikel Micro-abrasion/Corrosion Behavior of Pack-Borided AISI 316L Steel and ASTM F1537 CoCrMo Alloy in Ceramic-on-Ceramic Couplings

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Titel: Parametric Analysis for Erosion Wear of Waste Marble Dust-Filled Polyester Using Response Surface Method and Neural Networks
verfasst von: Sandip Kumar Nayak
Alok Satapathy
Sisir Mantry
Publikationsdatum: 08.03.2021
Verlag: Springer US
Erschienen in: Journal of Materials Engineering and Performance / Ausgabe 6/2021
Print ISSN: 1059-9495
Elektronische ISSN: 1544-1024
DOI: https://doi.org/10.1007/s11665-021-05595-6

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